Raisin

Donated on 8/13/2023

Images of the Kecimen and Besni raisin varieties were obtained with CVS. A total of 900 raisins were used, including 450 from both varieties, and 7 morphological features were extracted.

Dataset Characteristics

Multivariate

Subject Area

Biology

Associated Tasks

Classification

Feature Type

Real, Integer

# Instances

900

# Features

7

Dataset Information

Additional Information

Images of Kecimen and Besni raisin varieties grown in Turkey were obtained with CVS. A total of 900 raisin grains were used, including 450 pieces from both varieties. These images were subjected to various stages of pre-processing and 7 morphological features were extracted. These features have been classified using three different artificial intelligence techniques.

Has Missing Values?

No

Introductory Paper

Kuru Üzüm Tanelerinin Makine Görüşü ve Yapay Zeka Yöntemleri Kullanılarak Sınıflandırılması

By İ̇lkay Çinar, Murat Koklu, Sakir Tasdemir. 2020

Published in Gazi Journal of Engineering Sciences

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
AreaFeatureIntegerGives the number of pixels within the boundaries of the raisin.no
MajorAxisLengthFeatureContinuousIt measures the environment by calculating the distance between the boundaries of the raisin and the pixels around it.no
MinorAxisLengthFeatureContinuousGives the length of the main axis, which is the longest line that can be drawn on the raisin.no
EccentricityFeatureContinuousGives the length of the small axis, which is the shortest line that can be drawn on the raisin.no
ConvexAreaFeatureIntegerIt gives a measure of the eccentricity of the ellipse, which has the same moments as raisins.no
ExtentFeatureContinuousGives the number of pixels of the smallest convex shell of the region formed by the raisin.no
PerimeterFeatureContinuousGives the ratio of the region formed by the raisin to the total pixels in the bounding box.no
ClassTargetCategoricalKecimen and Besni raisin.no

0 to 8 of 8

Additional Variable Information

1.) Area: Gives the number of pixels within the boundaries of the raisin. 2.) Perimeter: It measures the environment by calculating the distance between the boundaries of the raisin and the pixels around it. 3.) MajorAxisLength: Gives the length of the main axis, which is the longest line that can be drawn on the raisin. 4.) MinorAxisLength: Gives the length of the small axis, which is the shortest line that can be drawn on the raisin. 5.) Eccentricity: It gives a measure of the eccentricity of the ellipse, which has the same moments as raisins. 6.) ConvexArea: Gives the number of pixels of the smallest convex shell of the region formed by the raisin. 7.) Extent: Gives the ratio of the region formed by the raisin to the total pixels in the bounding box. 8.) Class: Kecimen and Besni raisin.

Class Labels

Kecimen and Besni raisin

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Keywords

image processing

Creators

İ̇lkay Çinar

ilkay.cinar@selcuk.edu.tr

Selcuk University

Murat Koklu

mkoklu@selcuk.edu.tr

Selcuk University

Sakir Tasdemir

stasdemir@selcuk.edu.tr

Selcuk University

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